The Ecosystem in Communication Networks

REVIEW OF INTERNATIONAL GEOGRAPHICAL EDUCATION

ISSN: 2146-0353 ● © RIGEO ● 11(5), SPRING, 2021 www.rigeo.org Research Article The Ecosystem in Communication Networks

Sinta Paramita1 Engkus Kuswarno2 Doctoral Student of Communication, University Department of Communication, University Padjajaran [email protected] [email protected]

Agus Rusmana3 Eko Harry Susanto4 Department of Communication, University Faculty of Communication Science, Universitas [email protected] Tarumanagara [email protected]

Abstract This study aims to determine whether there are relationships formed in the communication ecosystem and whether there are dominant actors in the communication ecosystem through YouTube content. The Ecosystem is a reciprocal relationship and is interrelated with one another in an environment. Social media as a means of interaction in a virtual world makes it easy for anyone to interact. Viewers who like it will often comment, click, like, share on the content. Automatic actions become digital data that can be traced so that they can shape the communication ecosystem. One of them is the content that is the object of this research is the Lupis Mbah Satinem edition of the hawker series made by Netflix. The theory used in this research is Network Theory, which prioritizes actors and relationships in a network. The approach used is quantitative. This study uses the Communication Network Analysis (CNA) method with the Netlytic application as a tool for data collection techniques. The population and sample used in this study were 2,893 comments. The result of this research is that there is no relationship between nodes as indicated by the value of Reciprocity 0; it can be said that the Ecosystem is not formed in the YouTube content. However, there are five dominant actors in the material. The message conveyed through comments attracts the attention of other actors to form 5 clusters in CNA. The relation model that is obtained in the research is one mode and undirected relationship types.

Keywords ecosystem; communication; network; youtube

To cite this article: Paramita S, Kuswarno E, Rusmana A, and Susanto E, H. (2021). The Ecosystem in Communication Networks. Review of International Geographical Education (RIGEO), 11(5), 170-182. Doi: 10.48047/rigeo.11.05.17 Submitted: 20-01-2021 ● Revised: 15-02-2021 ● Accepted: 25-03-2021

Introduction

Based on Internet World Stats 2020 data, Asia ranks first internet users at 50.3%. Nearly half of the world's population who use the Internet are in Asia. Of the total percent, Indonesia contributed 7.4% or the equivalent of 2 million internet users throughout Indonesia (“Internet World Stats Usage and Populations Statistics,” 2020). A survey conducted by APJII in 2016, internet users in Indonesia reached 132.7 million people. An increase occurred in 2017 as many as 143.26 million people from the total population of Indonesia's 262 million people. This increase is increasing every year, seen from the latest data released by APJII in 2018, recording internet user penetration reaching 171.17 million people or an increase of 64.8% of Indonesia's total population of 254.17 million people (APJII, 2018). The 2 data above related to the development of the Internet in Indonesia brings several changes, especially in the event of Communication Science. The rapid development of communication and information technology has brought changes in the event of Communication Science research in various fields in Indonesia. For example, a study with the theme of developing adolescent interpersonal relationships in the use of social media in the city of Bandung, this research results in young attitudes that need to build interpersonal relationships in communicating using social media (Darmawan, Silvana, Zaenudin, & Effendi, 2019). Another research related to technological developments is the Portrait of Indonesia's Digital Society. This research results in 51% of urban internet users and 49% of rural internet users. The exciting thing is that rural communities have almost caught up with urban communities using the Internet. This shows that online penetration through IT infrastructure to rural areas in Indonesia is quite high (Kuswarno, 2015). Other research is related to negative things that are formed from daily life and technology, namely Fraud in interactions through social media, this research produces internal and external factors that can encourage victims to interact, impression creation strategies and framing strategies by fraudsters, and social media characteristics that can create the reality in the minds of victims of Fraud (Rusmana, 2015). Research related to media and government texts, such as research with the theme Cyber media, Apparatus, and Diversity News. This research results in the dynamics of the relationship between cyber media and government officials today in terms of reporting on the issue of diversity characterized by the closeness that was previously framed in power relations, currently in relationships of primary interests, accuracy, and risk management (Susanto, Loisa, & Junaidi, 2020). Research with the theme of Cyber media news coverage on diversity issues in Indonesia results that mass media do not fully support diversity, they are still found to be non-neutral in news coverage, this is indicated by the theme of coverage and some of its supporting elements (Loisa, Susanto, Junaidi, & Loekman, 2019). Furthermore, research related to media management information management in managing the reputation of the Indonesian Police has resulted in mass media collaboration and optimization of social media that can suppress negative news by using the IMM application which is proven to increase positive trends for the Police in Indonesia (Amar, Mulyana, Bajari, & Rizal, 2019). The six previous studies described above are related to the portrait of the use of technology in Indonesian society. This research will also raise technology with the theme of Digital Media Ecosystem for Viral Communication, particularly seeing content as the power to create issues. Digital Media Ecosystem, a group of researchers and practitioners first introduced the term digital Ecosystem from the Directorate- General for Communications Networks, Content, and Technology (DG-CONNECT), a European Commission institution. This group uses this term when designing a new ecosystem model that adopts the use of information and communication technology tools (Wisnuhardana, 2018). In the terminology of Biology, an ecosystem is a unitary system consisting of various communities of non- living organisms in the form of matter and energy and living organisms that are interconnected and interacting (G & Spoolman, 2012). Furthermore, the Ecosystem is commonly used in the field of biology to describe a reciprocal interaction between living things in an environment. More specifically, an ecosystem is the entire formation of living things (biomes) and their place of life (Kartawinata, 2010). Living things and the environment are two inseparable entities. Both have a dependence on one another. Living things such as humans need the background to live, socialize, interact, and survive by relying on natural resources produced by the environment. Likewise, the situation requires humans to maintain environmental wisdom so that it is always maintained. Ecosystems have a direct or indirect influence on human life (Seftyono, 2011). The development of communication and information technology today brings humans into a different ecosystem. The current Ecosystem, which is no longer concerned with environmental issues but

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has entered a virtual environment, has become a digital ecosystem. McLuhan once came up with the concept of media ecology that intersects ecosystems in this research. According to McLuhan, ecology is a study of the environment and its effects on people (Richard & Turner, 2017). In Greek, ecology is Oikos and logos. Oikos is a house or place to live, while logos is defined as knowledge or knowledge. The following is an illustration of the most potent Ecosystem in the field of Biology. From these two syllables, ecology is simply defined as the study of the organisms in which it lives. Or in general, ecology is the study of the reciprocal relationship between groups of plants and their environment (G & Spoolman, 2012). In short, the Ecosystem can also be defined as a system of organisms that always interact mutually with their environment. The concept of the Ecosystem is a broad concept, which forms the basis of ecological theory. The ecosystem concept emphasizes reciprocal relationships and is interrelated with one another in an environment. Adopting the thought of Biology in the realm of Communication Science certainly has its challenges, as a great science Communication Science can cover the breadth and depth of science. One of them is Biology. As part of the science of biology, ecosystems focus on the interactions and interrelationships between organisms. This is also the initial foothold in the communication process. Humans who are organisms interact and communicate with other humans in the ecological sphere. With the development of technology, human ecology began to shift towards a more abstract direction. Ecology as a science that studies the environment as a place to live for an organism, to virtual ecology. Humans begin to enter, live, grow, and develop in a virtual ecology and form a digital ecosystem. Network theory In the communication science tradition, it is divided into seven parts: the semiotic tradition, the phenomenological culture, the cybernetics tradition, the socio psychological tradition, the sociocultural tradition, the critical tradition, and the rhetorical tradition (Littlejohn, 2002). This research is included in the cybernetics tradition. Littlejohn further said that the cybernetics tradition is a complex system in which some elements are interconnected and influence each other. Communication is the elements that influence one another, form a group, and control the system to achieve goals or balance. Currently, the development of communication research in the cybernetics science tradition has begun to develop rapidly. This development can be seen in the research results. As an example of comparative content analysis research in exploring style and contextual factors that resonate with populist communication on social networking sites, this research suggests a way to understand populist connection as a discursive communication framework that can help reconcile different concepts of populism (Schmuck & Hameleers, 2019). Also, research on the theme Narrating network tells of the idea of networks that have become central to many human investigation fields and are said to have revolutionized everything from medicine to markets to military intelligence. This research aims to describe the protocol of narrative meaning that can be interpreted from the network image and the context in which it is embedded, and discusses five types of network narrative reading, which are illustrated by the analysis of examples from journalism. To advance and broaden research around the defining aspects of visual culture after the digital transition (Bounegru, Venturini, Gray, & Jacomy, 2017). Other research addresses similar themes such as digital technology and big data rapidly changing the humanitarian crisis response and changing the traditional roles and power of the actors, the result of this research is to describe journalists can find challenges and opportunities in an environment where many crisis actors take over some—the role of the media (Chernobrov, 2018). The research was also carried out in Norway on the theme of Learning policies in Norwegian school reform: social network analysis on 2020 incremental improvements. This study resulted in about 70% of the reference texts published in Norway. Finally, social network analysis allows the authors to identify five influential texts that bridge the curriculum with the topic of quality monitoring reform (Baek et al., 2018). In addition to research related to the analysis of the social network Twitter: Mapping the digital humanities community, this research produces linguistic groups as a critical factor in explaining groupings in networks whose characteristics look similar to the small world (Grandjean, 2016). Of the five previous studies that refer to network theory. Researchers believe that network theory can develop in line with the development of communication and information technology in Indonesia. The Ecosystem described above indicates that humans, as living creatures, cannot communicate and not interact with other humans. As social beings, humans need other humans to communicate and interact. There are contacts and relationships of various forms, creating an interacting communication network. There are two main keywords in defining a communication network. First, the communication network is a phenomenon from the micro (actor) side, not a macro. Second, looking at the relationship between how these actors interact (Eriyanto, 2014). Many researchers use network analysis as a research method from a variety of multidisciplinary

172 Paramita S, Kuswarno E, Rusmana A, and Susanto E, H. (2021). The Ecosystem in Communication …

disciplines, ranging from psychology, sociology, anthropology, and communication. This method is commonly known as the Social Network Analysis (SNA), while in the field of communication, it is called the Communication Network Analysis (CNA). The difference between SNA and CNA emphasizes the application of SNA in communication research methods to identify communication structures in a system, and the relationship between communication flows between communicators and communication (Eriyanto, 2014). Not only CNA, but SNA was also developed in other communication methods such as the application of discourse networking analysis as a form of methodological development in social situations. This research provides new knowledge related to current methodological events (Pratama & Ulfa, 2017). Another study that is also developing in this context is collaborating digital rhetoric with SNA. This research seeks to describe the complexity of network communication in content (Paramita & Irena, 2020). There are four characteristics of the network method, as follows (Eriyanto, 2014): (1) Relationships, network research, emphasize the role of actors. In this case, actors can include individuals, institutions, states, and so on, informed links. (2) Network, together with a relationship originating from an actor who is an affiliate of a system. The emphasis leads to what actor’s form fingers. (3) Relationships in a relational context. Relations between actors or called relational. In this case, the actor is not independent but has a relationship with other actors in the network. (4) Relations and Structure, these relations can be seen from a structural perspective. Other actors in the Structure determine actor positions. This form will limit the space for the actor to move. To facilitate the use of CNA, it is necessary to know the basic concept terms used in SNA. The conditions in the CNA adopted from the words in the SNA can be described as follows (Bratawisnu & Alamsyah, 2018): Nodes represent positions owned by actors or users in a network. Nodes represent actors in a system, usually represented by black dots. Furthermore, the size of the nodes can be divided into two parts, namely indegree and outdegree. Indegree is a node that has many lines entering the node. This shows the popularity of a node that is always producing and receiving information. Meanwhile, outdegree is a node with several ranges coming out of a node (Netlytic, 2020). Edges describe the relationships between actors or users in a network. A line usually denotes them. Average degree is the number of relationships or reciprocity at one node or points divided by the number of links or reciprocity that occurs in one social network. Diameter is the farthest distance between two adjacent nodes. Average Path Length is the average geodesic distance or the ordinary path. In addition to knowing these terms, it is also necessary to understand the types of relations formed in the network, which will be described as follows (Eriyanto, 2014): One mode is a network of actors or nodes having the same type or the same position, for example, relationships between individuals or organizations. Whereas Two-mode is a network of actors or nodes that have different types in a system, for instance, in a network, there are actors or nodes and also institutions that are denoted by nodes as well. Relationships between actors or nodes have direction and have no course. This shape is usually marked with a line of arrows. A network that has a path usually consists of the sender and receiver of the message. A directionless system has no message sender and recipient. This shape is generally represented by lines between nodes, such as links or edges. Similar to directed and undirected, but the difference is that there is reciprocity in two directions between actors or nodes, which are denoted by two arrows at each end of the line. Meanwhile, asymmetry emphasizes actors or nodes who have roles, and other actors or nodes do not play a role. It is symbolized by an arrow pointing to one goal. This relationship focuses on the intensity of the relationship that occurs between actors or nodes. The researcher can choose between writing down the value that occurs in each interaction or not depending on the research plan that the researcher wants to develop. YouTube is a social media that focuses on video content. In 2005 three PayPal employees, namely Chand Hurley, Steve Chen, and Jawed Karim, formed a platform to share experiences via video (Wisnuhardana, 2018). In 2019 YouTube became the number one social media in Indonesia with the most access. It was recorded that 88% of the total social media users in Indonesia (Katadata.co.id, 2019). Even in 2020, YouTube still ranks first in Indonesia's most accessed social media (Databoks.katadata.co.id, 2020). In the study of Communication Science, YouTube has a huge role. It can be seen from the research developed via YouTube. Like research on consumer representation of genetic DNA testing on YouTube, this study produces videos of individuals using results, questioning, and questioning previously held conceptions of ethnic or ethnic identity (Marcon, Rachul, & Caulfield, 2020). Other research related to video content for teenagers on YouTube has shown that YouTube users show great interest in learning information related to education and avoid harmful content (García Jiménez & Montes Vozmediano, 2020). Currently, the presence of YouTube brings various kinds of hopes and opportunities for millennial generations to create and innovate through video 173 © RIGEO ● Review of International Geographical Education 11(5), SPRING, 2021 content. Nowadays, we can easily find Indonesian YouTubers who have successfully achieved wealth from creating content on YouTube. Not only individually, but YouTube is also used by profit and non-profit organizations to grab the virtual community's attention. One of the profit organizations that use YouTube to capture more viewers is Netflix. Almost similar to YouTube, Netflix is an exclusive platform that is useful for viewing both national and international films. The only difference is in the content. Content on Netflix is not as flexible as video content on YouTube.

Figure 1. Street Food Asia Source: www.netflix.com

As a platform that displays movies. Netflix also produces various films that are broadcast explicitly on Netflix, ranging from dramas, films, documentaries, and others. One of the documentary series films shown on Netflix is Street Food Asia. This documentary series presents nine snacks from the market on the streets of Asia, such as Thailand, Japan, India, Indonesia, Taiwan, South Korea, Vietnam, Singapore, and the Philippines. One exciting thing about this documentary series is the content of market snacks from Indonesia, namely the Lupis cake seller Mbah Satinem in Yogyakarta, Indonesia. This documentary serial film tells the story of a grandmother who struggles to survive by selling Lupis cakes in Yogyakarta.

Figure 2. Twogether series Source: Liputan6.com

His appearance on Netflix had made Mbah Satinem's name viral in the virtual universe. As; Food Bloggers, YouTubers, Instagram, content writers, and Online Media have flocked to create content about Mbah Satinem. The attraction of Mbah Satinem's content which received a good response from the virtual community in Indonesia was used by Netflix again by making the title Twogether, played by Lee Seung Gi and Jasper Lie, this show to appear on Netflix in 2020, taking

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one location in Yogyakarta and where Mbah Satinem (Asih, 2020). Besides that, but Netflix Indonesia also creates new content with the theme From Sense Down to the Heart - A Gift for Mbah Satinem from Chef Yuda Bustara. From some of the content that carries the theme of Mbah Satinem and Lupis cake, it can show that the content has an appeal to the virtual community in Indonesia. From the background explanation above, this research wants to know whether there is a relationship built in the communication ecosystem on YouTube content and whether the dominant actors form groups in a communication ecosystem on YouTube content.

Method

The approach used in this research is qualitative by using the network analysis method or Communication Network Analysis (CNA) using the Netlytic application. There are three levels of analysis in the network (Eriyanto, 2014): The level of actor (nodes) analysis that is the center of attention of a network. The level of group analysis focuses on the network formed of two or more actors. The system analysis level focuses on the network formed from the population. This type of research is descriptive, describing the Structure and actors in the network in detail. The objects used in this study are the two YouTube content below: This section should explain the method, sampling technique, data collection, and data analysis. Title: 58 Years of Selling This! Buy this with a number card - They Call it Lupsi Satinem # 578, Production of Hobby Manakan, link https://www.YouTube.com/watch?v=H3dvfuEEcpE. The reason for choosing this object is that Netflix is reproducing the documentary series content through the YouTube platform. The other viral content of Mbah Satiem on the Internet invites other YouTubers to create similar content. Researchers chose the YouTube account for eating hobbies besides the account is verified. The report ranks second on YouTube after Netflix Indonesia in content with the theme Mbah Satinem has 2,893 comments and 770,106 viewers. The subject of this research is a network that is formed in the two materials using the Netlytic application. Netlytic is a tool to find communication networks among community members, especially those connected to the network (Gruzd & Haythornthwaite, 2013). The following is a data collection technique which will be described as follows: Determine the YouTube content be analyzed. Using the Netlytic application which can be seen at the link https://netlytic.org/index.php Classify the data into two parts: the name of the network and who is hiding who is in the network. Second is a chain network, which reciprocates to whom. Make two data classifications, namely First, the name of the network, and who is greeting who is on the network. This section is a communication network built for each YouTube content to find how the content relates to the audience. The second is a chain network, who is replying to whom. This section, called the chain network, sees the audience's movement interacting with other audiences based on the audience uploading to the YouTube account. The analysis technique used in this research is the measuring instrument used in these two categories, which are as follows based on the exposure (Netlytic, 2020): Centralization, measuring the average level of centralization to actors or nodes in the network. The value of centralization is said to be high if it is close to number 1, which shows that actors or nodes also dominate. Density measures the density of the bonds that exist in the network. The closer this measurement is to the value of 1, the closer the relationship or conversation will be, which shows participants talking a lot to other people. On the other hand, if the value is 0, then it indicates that there is almost no connection with other people in the network. Reciprocity measures the reciprocity of bonds in the network. High scores indicate that many participants are having a two-way conversation. Meanwhile, a low value indicates a lot of one-sided conversations. Modularity measures the size of the node size on a cluster that represents communication. A higher value for modularity indicates a clear split between the communities served by the grouping on Netlytic. A low amount of modularity, usually less than 0.5, means that the clusters found by Netlytic will overlap more, the network is more likely to consist of a core group of nodes. The diameter is measuring the longest distance between two network participants. This measure shows the network's size by counting the number of nodes required to move from one side to another.

Result and Discussion

Below is YouTube content which is analyzed as follows: The analysis will be grouped into two parts. The first is the name of the network and who is hiding who is in the network. Second is a chain network, which reciprocates to whom.

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Table 1. Communication Network Analysis Hobbies Eating

Account Hobbi Makan 58 Tahun Berjualan ini! Beli ini Pake Kartu Nomor – Content They Call it Lupsi Satinem #578 https://www.YouTube.co Link m/watch?v=H3dvfuEEcpE Publish April 6, 2019 Viewer 770.110 Comments 2.893 Unique 2568 Poster source: YouTube, 2020

The name of the network and who is hiding who is on the network

Figure 3. The name of the network and who is hiding who is on the network Source: Netlytic.com

Using the Fruchterman Reingold layout model, you can see the names of the actors who are most often inserted in every comment on the YouTube content network. Here is a list of actors with the most replayed names on the network:

Figure 4. Figure 4: DHC Hariyono node at most mention in comments four times. Source: Netlytic.com

Below is an example of a comment adding the name Hariyono DHC in a comment on YouTube.

176 Paramita S, Kuswarno E, Rusmana A, and Susanto E, H. (2021). The Ecosystem in Communication …

Comments came from Mella Della Rosha's account on May 13, 2019: "Hariyono DHC, an Indonesian, must be Polite."

Figure 5. Mansion comments from Mella Della Rosha's account Source: Netlytic.com The following are the top ten posters in Card Number content - They Call it Lupsi Satinem # 578 on YouTube. Hariyono DHC's account ranks first as much as 14.7%, the center of attention for reports on the content.

Figure 6. top ten posters Source: Netlytic.com The following are the results of the analysis calculated by the Netlytic system: (1) Diameter: 3. (2) Density: 0.000002. (3) Reciprocity: 0.00000. (4) Centralization: 0.000391. (5) Modularity: 0.816600 The diameter shows the number 3, which means it is the longest distance between nodes through the three nodes. Density indicates the name 0.000002, which shows the size of the density complementing the diameter. The closer the measurement is indicated by a value of 1. The density value suggests there is no closeness between the nodes. Reciprocity shows the number 0.00000, which means there is no two-way conversation. Centralization shows the number 0.000391, which means that several accounts become central, which dominate if it is close to number 1. However, if it is close to the number 0, it means decentralized, which means that information flows freely. Modularity shows the number of 0.816600, which means there are clear divisions between the clusters. Low modularity values are usually less than 0.5. Chain network, who reciprocates to whom.

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Figure 7. Chain network, who reciprocates to whom. Source: Netlytic.com

By using the Fruchterman Reingold layout model, five node clusters get the most responses from other nodes. Here the researcher will present the 2 clusters that get the most answers, namely as follows: Cluster 1: Shahrul Romadhan's account with 84 points indegree. This shows as many as 84 accounts replay. They are shown in pink.

Figure 8. Cluster 1 Source: Netlytic.com

Below is an example of a replay of Joel Alta's account to Syahrul Romadhan's account. Replay messages from Joel Alta Mella Della Rosha's report on July 4, 2019: "Shahrul Romadhan agrees."

Figure 9. Comment cluster 1 Source: Netlytic.com

Cluster 2: Revelation L4 account with 31 senses points. This shows as many as 31 accounts replay syahrul Romadhan account posts. They are shown in blue.

Figure 10. Cluster 2 Source: Netlytic.com

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Below an example of a replay of the Yenni Yenni account to the L4 revelation account. Replay messages from Joel Alta Mella Della Rosha's report on March 3, 2020, are as follows: "Wahyu L4 agrees ... his grandmother, who is old, is so physically still healthy ... nowadays, people aged 50 and under have died."

Figure 11. Comment cluster 2 Source: Netlytic.com

The following are the results of the analysis calculated by the Netlytic system: (1) Diameter: 8. (2) Density: 0.000059. (3) Reciprocity: 0.00000. (4) Centralization: 0.016450. (5) Modularity: 0.885400 The diameter shows the number 8, which means it is the longest distance between nodes through 8 nodes. Density indicates the name 0.000059, which shows the size of the frequency complementing the diameter. The closer the measurement is indicated by a value of 1. The density value suggests there is no closeness between the nodes. Reciprocity shows the number 0.00000, which means there is no two-way conversation. Centralization shows the number 0.016450, which means that several accounts become central, which dominate if it approaches number 1. It can be seen from the Syahrul Romadhan account, and the L4 Revelation account the center of attention in the content. However, if it is closer to the number 0, it means decentralized, which means that information flows freely. Modularity shows the number of 0.885400, which means that there are clear divisions between the clusters. Low modularity values are usually less than 0.5.

The Ecosystem in Communication Network Analysis

To answer the formulation of the problem in this study, namely how the communication ecosystem can be formed in the content on YouTube has been described in the Communication Network Analysis above, which will be described as follows: There is no reciprocal relationship between nodes. Interesting in the analysis results above is that social media as a means of interaction between users does not appear to have a reciprocal relationship between accounts or nodes. The value of reciprocity indicates this, both in the network name category and hiding who is in the network and the chain network category, who replies to whom. The data obtained is more likely to give a response from a message without any feedback. The type of network relationship formed is one mode, which shows the network between nodes has the same position. The kind of relationship that is created also shows undirected or a relationship that has no direction. Some nodes are the center of attention. There are five clusters formed on the comments that appear on YouTube, namely Syahrul Romadhan Point 84 Inderee, Wahyu L4 Point 31 Inderee, Alif Ahmad Point 25 Inderee, Achad Dafig Point 22 Inderee, and Yummyboy Point 21 Indegree. Of the 2,893 comments contained in the content, five groups formed the center of attention. The feedback that is the center of awareness of the nodes are as follows: "said she was disgusted by her hand ... here let me get your nutmeg ... I've eaten for 20 years with my parents' hands, so it's healthy, not sick," Wahyu L4's account. “Yg bilang jijik sama tangan Mbah nya…sini biar gw getok pala lu.. Gw udah makan 20 tahun dengan tangan orang tua malah sehat bukan malah sakit” akun wahyu L4. "If you have 1 million subscribers, please replace it, bro, so that his wife who holds the camera, Evan, will try the food" Syahrul Romadhan's account.“Kalau sudah 1jt subscriber, gatian ya mas bro biar istri nya yang pegang kamera bg evan yang nyoba makanannya” akun syahrul romadhan. "Who agrees if this Chanel is fast 5 million subs?" alif Ahmad's account.“siapa yang setuju kalu Chanel ini cepet 5 jt subs?” akun alif ahmad "Saying disgust means that you have never been bribed using your mother/mother's hand ... those who agree to like" achad account dafig “Yg bilang jijik berarti sodara belum pernah di Suapin pake Tangan Ibu/Mama loh..yang setuju like” akun achad dafig "I love Indonesia. I love Indonesian food from Korea" yummy boy account. These five comments have an appeal to the content audience, with various responses. The following are the 30 words most written nodes that are presented in the word cloud data, including 179 © RIGEO ● Review of International Geographical Education 11(5), SPRING, 2021

the following: (1) Bang: 896 nodes. (2) Evan: 383 nodes. (3) Feeds: 236 nodes. (4) Mbah: 181 nodes. (5) Snacks: 152 nodes.

Figure 11. Cloud data Source: Netlytic.com

The word "bang" is usually used to refer to a brother or a nickname for a man in several regions in Indonesia. This word is intended for the Hobby Makan account owner on YouTube, who created the content. Not only word cloud data calculations, but ICTA Visualization data also shows the same thing. Namely, the word "bang" is the word most written by accounts or nodes in response to various messages. The following is a chart of the ICTA Visualization.

Figure 12. ICTA Visualization “bang” word Source: Netlytic.com

The Ecosystem that is formed in each content on social media, especially on YouTube, is unique. This can be seen from the content created and the response issued to that content to answer the formulation of the problem raised in this study. With the development of communication and information technology, it is also easier for researchers in the field of Communication Sciences to manage virtual data. Various applications are offered to assist researchers in maintaining the desired data. This research tries to use these tools to see the Ecosystem that is formed in content. This research is expected to provide input and enrich communication research, especially in understanding communication technology and developing research methods in communication.

Conclusion

This study concludes that there is no reciprocal relationship between nodes; the value of Reciprocity 0 indicates this. It can be said that the Ecosystem in YouTube content with the theme "Use a Number Card - They Call it Lupsi Satinem # 578" was not formed, because there was no reciprocal relationship. Back. However, within the same content, clusters, or groups that become the center of attention appears, actors or nodes point to these 5 clusters with replay or mention of account names in comments. This means that the power of messages through comments can provoke interaction. The relation model formed in this study is the one mode and undirected relationship types.

Limitation and Study Forward

The limitation of this study is that the researcher only focuses on one account on YouTube in depth. The recommendation for further research is to be able to develop several reports that can produce more data.

Acknowledgements

180 Paramita S, Kuswarno E, Rusmana A, and Susanto E, H. (2021). The Ecosystem in Communication …

Thanks, are given to the Padjajaran University Bandung Doctoral Program for supporting this research to be realized and for the research object used in this research.

References

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